• 대한수학회보
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• 제38권1호
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• pp.157-161
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• 2001

In this paper, we prove that an affine manifold M is finitely covered by a manifold $\overline{M}$ where $\overline{M}$ is radiant or the tangent bundle of $\overline{M}$ has a conformally flat vector subbundle of the projective holonomy group of M admits an invariant probability Borel measure. This implies that$x^M$is zero.

• 한국동물위생학회지
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• 제42권3호
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• pp.145-152
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• 2019

Canine vector-borne diseases (CVBDs) are transmitted by different groups of hematophagous arthropod vectors that are distributed worldwide and can cause significant health problems for dogs. The aim of this study was to investigate and compare the prevalence of selected CVBD pathogens in rural outdoor dogs based on prevention status. Between June 2017 and February 2019, blood samples were collected from 343 clinically healthy rural dogs composing two different groups: systematically managed dogs (SMD; n=92) and personally managed dogs (PMD; n=251). Vaccination and preventive medications were applied strictly following the programmed schedule for the SMD group; in contrast, in the PMD group, they were applied only when requested by the dog owners. Serological and molecular assessments showed that significantly more dogs in the PMD group were infected with B. gibsoni (P<0.001) and D. immitis (P=0.001) than those in the SMD group. These findings suggest that the regular use of preventive medications and environmental controlling efforts contribute to reducing the prevalence of CVBD pathogen infections. In addition, dogs infected with certain kinds of CVBD pathogens could remain asymptomatic, suggesting that continuous monitoring and periodic preventive treatment should be conducted even for clinically healthy dogs.

• Parasites, Hosts and Diseases
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• 제49권1호
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• pp.59-64
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• 2011

A survey of mosquitoes, including the vector status of Brugia malayi filariasis and their relative larval density, was conducted from 2002 to 2005 at several southern remote islands of Jeollanam-do (province), Gyeongsangnam-do, and Jeju-do, Korea, where filariasis was previously endemic. Overall, a total of 9 species belonging to 7 genera were collected. Ochlerotatus togoi (formerly known as Aedes togoi), Anopheles (Hyrcanus) group, and Culex pipiens were the predominant species captured at all areas. Oc. togoi larvae were most frequently collected at salinity levels < 0.5% during June and July, with densities decreasing sharply during the rainy season in August. The most likely explanation for the eradication of filariasis in these areas is suggested to be an aggressive treatment program executed during the 1970s and the 1990s. However, high prevalence of the vector mosquitoes may constitute a potential risk for reemerging of brugian filariasis in these areas.

• Journal of the Korean Data and Information Science Society
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• 제13권2호
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• pp.65-75
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• 2002

The support vector machine (SVM), first developed by Vapnik and his group at AT &T Bell Laboratories, is being used as a new technique for regression and classification problems. In this paper we present an approach to estimating approximate prediction intervals for SVM regression based on posterior predictive densities. Furthermore, the method is illustrated with a data example.

• 대한수학회논문집
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• 제10권4호
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• pp.949-961
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• 1995

Let f be a smooth G map from a nonsingular real algebraic G variety to an equivariant Grassmann variety. We use some G vector bundle theory to find a necessary and sufficient condition to approximate f by an entire rational G map. As an application we algebraically approximate a smooth G map between G spheres when G is an abelian group.

• Communications for Statistical Applications and Methods
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• 제6권2호
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• pp.337-344
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• 1999

Support vector machine(SVM) is a new and very promising regression and classification technique developed by Vapnik and his group at AT&T Bell laboratories. This article provides a brief overview of SVM focusing on linear regression. We explain from statistical point of view why SVM might be attractive and how this could be compared with other linear regression techniques. Furthermore. we explain model selection based on VC-theory.

• Architectural research
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• 제14권4호
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• pp.143-152
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• 2012

A form-finding procedure is presented for planar tensegrity structures. Notably, a simple decision criteria is proposed to select the desirable candidate position vector from the unitary matrix produced by the eigenvalue decomposition of force density matrix. The soundness of the candidate position vector guarantees faster convergence and produces a desirable form of tensegrity without any member having zero-length. Several numerical examples are provided to demonstrate the capability of the proposed form-finding process.

• Architectural research
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• 제13권1호
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• pp.17-24
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• 2011

Isogeometric solution of Poisson equation is provided. NURBS (NonUniform B-spline Surface) is introduced to express both geometry of structure and unknown field of governing equation. The terms of stiffness matrix and load vector are consistently derived with very accurate geometric definition. The validity of the isogeometric formulation is demonstrated by using two numerical examples such as square plate and L-shape plate. From numerical results, the present solutions have a good agreement with analytical and finite element (FE) solutions with the use of a few cells in isogeometric analysis.

• Communications for Statistical Applications and Methods
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• 제6권3호
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• pp.813-820
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• 1999

Supprot vector machine(SVM) is a new and very promising regression and classification technique developed by Vapnik and his group at AT&T Bell Laboratories. in this paper we provide a brief overview of SVM for regression. Furthermore we describe Bayesian model selection based on macKay's evidence framework for SVM regression.

• Communications for Statistical Applications and Methods
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• 제6권3호
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• pp.843-848
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• 1999

Support vector machine(SVM) is a new and very promising classification and regression technique developed by Bapnik and his group at AT&T Bell laboratories. However it has failed to establish itself as common machine learning tool. This is partly due to the fact that SVM is not easy to implement and its standard implementation requires the optimization package for quadratic programming. In this paper we present simple iterative Kernl Adatron algorithm for nonparametric regression which is easy to implement and guaranteed to converge to the optimal solution and compare it with neural networks and projection pursuit regression.